A Brief Review on Brain Tumor Segmentation and Detection Algorithms with Issues and Challenges

Computer vision techniques and development of computer-aided tools are evolving as the areas of research for automatic segmentation of brain tumors. Some of these techniques showed good results but there is no winning technique as these approaches have often not used practically in hospitals. In these days, research on medical healthcare system [1] is an emerging area and main focused on the designing of an efficient segmentation approach with concept of Artificial Intelligence (AI) techniques for appropriate region and fast segmentation purpose. There are a lots of clustering as well as traditional segmentation approaches are available for medical images, but most of them are depended on the data types. In this paper, we presented a brief review on clustering-based medical image segmentation with their challenging factors faced by researchers [2]. Due to high success rate of AI, Deep Learning (DL) algorithms, there has been a considerable amount of brain tumor segmentation works are aimed by researcher and try to solve the exiting challenges. In this survey, various type of brain tumor segmentation and detection system are analyzed to find out the exact tumor location and faced issued by the researchers. In Addition, some challenging factors are also analyzed with various algorithms of segmentation such DL, K-means clustering, Optimization and traditional approaches.